
"""simple docstring"""



import warnings
from typing import List, Optional, Tuple, Union

import numpy as np
import PIL
import torch

from ...models import UNetaDModel
from ...schedulers import RePaintScheduler
from ...utils import PIL_INTERPOLATION, logging, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


a       :						Dict       		=		logging.get_logger(__name__)  # pylint: disable=invalid-name


def 			_UpperCamelCase						(    _A   )     ->	Optional[int]:





    """simple docstring"""

    warnings.warn(
        """The preprocess method is deprecated and will be removed in a future version. Please"""
        """ use VaeImageProcessor.preprocess instead"""       ,   _A       ,   )
    if isinstance(_A       ,   torch.Tensor   ):
        return image
    elif isinstance(_A       ,   PIL.Image.Image   ):
        _UpperCAmelCase        = [image]

    if isinstance(image[0]       ,   PIL.Image.Image   ):
        _UpperCAmelCase     ,_UpperCAmelCase        = image[0].size
        _UpperCAmelCase     ,_UpperCAmelCase        = (x - x % 8 for x in (w, h))  # resize to integer multiple of 8

        _UpperCAmelCase        = [np.array(i.resize((w, h)       ,   resample=PIL_INTERPOLATION["""lanczos"""]   )   )[None, :] for i in image]
        _UpperCAmelCase        = np.concatenate(_A       ,   axis=0   )
        _UpperCAmelCase        = np.array(_A   ).astype(np.floataa   ) / 255.0
        _UpperCAmelCase        = image.transpose(0       ,   3       ,   1       ,   2   )
        _UpperCAmelCase        = 2.0 * image - 1.0
        _UpperCAmelCase        = torch.from_numpy(_A   )
    elif isinstance(image[0]       ,   torch.Tensor   ):
        _UpperCAmelCase        = torch.cat(_A       ,   dim=0   )
    return image


def 			_UpperCamelCase						(    _A   )     ->	int:





    """simple docstring"""

    if isinstance(_A       ,   torch.Tensor   ):
        return mask
    elif isinstance(_A       ,   PIL.Image.Image   ):
        _UpperCAmelCase        = [mask]

    if isinstance(mask[0]       ,   PIL.Image.Image   ):
        _UpperCAmelCase     ,_UpperCAmelCase        = mask[0].size
        _UpperCAmelCase     ,_UpperCAmelCase        = (x - x % 3_2 for x in (w, h))  # resize to integer multiple of 32
        _UpperCAmelCase        = [np.array(m.convert("""L"""   ).resize((w, h)       ,   resample=PIL_INTERPOLATION["""nearest"""]   )   )[None, :] for m in mask]
        _UpperCAmelCase        = np.concatenate(_A       ,   axis=0   )
        _UpperCAmelCase        = mask.astype(np.floataa   ) / 255.0
        _UpperCAmelCase        = 0
        _UpperCAmelCase        = 1
        _UpperCAmelCase        = torch.from_numpy(_A   )
    elif isinstance(mask[0]       ,   torch.Tensor   ):
        _UpperCAmelCase        = torch.cat(_A       ,   dim=0   )
    return mask







class 		a_    (			_UpperCAmelCase						):
    a   :					UNetaDModel
    a   :					RePaintScheduler







    def __init__(      self      :							str  ,       __UpperCamelCase      :							int  ,       __UpperCamelCase      :							Union[str, Any]  )		->Union[str, Any]:






        '''simple docstring'''






        super().__init__()
        self.register_modules(unet=__UpperCamelCase  ,       scheduler=__UpperCamelCase  )







    @torch.no_grad()
    def __call__(      self      :							List[str]  ,       __UpperCamelCase      :							Union[torch.Tensor, PIL.Image.Image]  ,       __UpperCamelCase      :							Union[torch.Tensor, PIL.Image.Image]  ,       __UpperCamelCase      :							int = 2_50  ,       __UpperCamelCase      :							float = 0.0  ,       __UpperCamelCase      :							int = 10  ,       __UpperCamelCase      :							int = 10  ,       __UpperCamelCase      :							Optional[Union[torch.Generator, List[torch.Generator]]] = None  ,       __UpperCamelCase      :							Optional[str] = "pil"  ,       __UpperCamelCase      :							bool = True  ,       )		->Union[ImagePipelineOutput, Tuple]:






        '''simple docstring'''






        _UpperCAmelCase        = image

        _UpperCAmelCase        = _preprocess_image(__UpperCamelCase  )
        _UpperCAmelCase        = original_image.to(device=self.device  ,       dtype=self.unet.dtype  )
        _UpperCAmelCase        = _preprocess_mask(__UpperCamelCase  )
        _UpperCAmelCase        = mask_image.to(device=self.device  ,       dtype=self.unet.dtype  )

        _UpperCAmelCase        = original_image.shape[0]

        # sample gaussian noise to begin the loop
        if isinstance(__UpperCamelCase  ,       __UpperCamelCase  ) and len(__UpperCamelCase  ) != batch_size:
            raise ValueError(
                f"""You have passed a list of generators of length {len(__UpperCamelCase  )}, but requested an effective batch"""
                f""" size of {batch_size}. Make sure the batch size matches the length of the generators."""  )

        _UpperCAmelCase        = original_image.shape
        _UpperCAmelCase        = randn_tensor(__UpperCamelCase  ,       generator=__UpperCamelCase  ,       device=self.device  ,       dtype=self.unet.dtype  )

        # set step values
        self.scheduler.set_timesteps(__UpperCamelCase  ,       __UpperCamelCase  ,       __UpperCamelCase  ,       self.device  )
        _UpperCAmelCase        = eta

        _UpperCAmelCase        = self.scheduler.timesteps[0] + 1
        _UpperCAmelCase        = generator[0] if isinstance(__UpperCamelCase  ,       __UpperCamelCase  ) else generator
        for i, t in enumerate(self.progress_bar(self.scheduler.timesteps  )  ):
            if t < t_last:
                # predict the noise residual
                _UpperCAmelCase        = self.unet(__UpperCamelCase  ,       __UpperCamelCase  ).sample
                # compute previous image: x_t -> x_t-1
                _UpperCAmelCase        = self.scheduler.step(__UpperCamelCase  ,       __UpperCamelCase  ,       __UpperCamelCase  ,       __UpperCamelCase  ,       __UpperCamelCase  ,       __UpperCamelCase  ).prev_sample

            else:
                # compute the reverse: x_t-1 -> x_t
                _UpperCAmelCase        = self.scheduler.undo_step(__UpperCamelCase  ,       __UpperCamelCase  ,       __UpperCamelCase  )
            _UpperCAmelCase        = t

        _UpperCAmelCase        = (image / 2 + 0.5).clamp(0  ,       1  )
        _UpperCAmelCase        = image.cpu().permute(0  ,       2  ,       3  ,       1  ).numpy()
        if output_type == "pil":
            _UpperCAmelCase        = self.numpy_to_pil(__UpperCamelCase  )

        if not return_dict:
            return (image,)

        return ImagePipelineOutput(images=__UpperCamelCase  )

"""simple docstring"""



import warnings
from functools import wraps
from typing import Callable


def 			_UpperCamelCase						(    _A   )     ->	Callable:





    """simple docstring"""

    @wraps(_A   )
    def _inner_fn(*_A       ,   **_A   ):
        warnings.warn(
            (F"""'{fn.__name__}' is experimental and might be subject to breaking changes in the future.""")       ,   _A       ,   )
        return fn(*_A       ,   **_A   )

    return _inner_fn